The traditional supply chain system included smart objects to enhance intelligence, automation capabilities, and intelligent decision-making. Internet of Things (IoT) technologies are providing unprecedented opportunities to enhance efficiency and reduce the cost of the existing system of the supply chain. This article aims to study the prevailing supply chain system and explore the benefits obtained after smart objects and embedded networks of IoT are implanted. Short-range communication technologies, radio frequency identification (RFID), middleware, and cloud computing are extensively comprehended to conceptualize the smart supply chain management system. Moreover, manufacturers are achieving maximum benefits in terms of safety, cost, intelligent management of inventory, and decision-making. This study also offers concepts of smart carriage, loading/unloading, transportation, warehousing, and packaging for the secure distribution of products. Furthermore, the tracking of customers to convince them to make more purchases and the modification of shops with the assistance of the Internet of Things are thoroughly idealized.

Sustainability is one of the fastest-growing research areas globally. Irrespective of industry and economic activity, it is the need of the day. This study examines the impact of green process innovation and green production on sustainability in Pakistan and India’s cement and plastic manufacturing industries. The study also addresses the moderating role of environmental awareness, which increases the effect of green productivity and green innovation towards sustainability. The research is based on a quantitative approach to addressing the issue in question. Primary data were collected via a closed-ended questionnaire from 657 employees of Pakistan and India’s plastic and cement manufacturing industries, and were analyzed via partial least square structural equation modeling via SmartPLS. The findings show that green productivity and green process innovation have a significant impact on sustainability, while environmental awareness also plays a significant role in sustainable practices in the cement and plastic manufacturing industries of Pakistan and India. The results are helpful for policymakers, industries, and other governmental and non-governmental organizations to ensure sustainability through green process innovation, green productivity, and environmental awareness.

The internet of things (IoT) is one of the growing platforms of the current era that has encircled a large population into its domain, and life appears to be useless without adopting this technology. A significant amount of data is generated from an immense number of smart devices and their allied applications that are constructively utilized to automate our daily life activities. This big data requires fast processing, storage, and safe passage through secure channels to safeguard it from any malicious attacks. In such a situation, security is considered crucial to protect the technological resources from unauthorized access or any interruption to disrupt the seamless and ubiquitous connectivity of the IoT from the perception layer to cloud computers. Motivated by this, this article demonstrates a general overview about the technology and layered architecture of the IoT followed by critical applications with a particular focus on key features of smart homes, smart agriculture, smart transportation, and smart healthcare. Next, security threats and vulnerabilities included with attacks on each layer of the IoT are explicitly elaborated. The classification of security challenges such as confidentiality, integrity, privacy, availability, authentication, non-repudiation, and key management is thoroughly reviewed. Finally, future research directions for security concerns are identified and presented.

The use of Telecommunication services has been strikingly increased in recent decade. Hence it has become crucial to investigate the reasons behind telecom usage patterns and understand factors playing an important role for perception of telecom services. These investigations are important for both consumers and telecommunication service provider in order to build a strong relationship towards better delivery of telecom services which may result in satisfied customer. The developed model of this research is consisting of five variables which have a lot of impact on perception of consumer towards adoption of telecom services. The results of this research have numbers of practical applications both for marketer and telecom service consumer.

In pipelines and process equipment, especially in cold oceanic environments, gas hydrate development presents a serious problem to the petroleum industry. Getting around this problem efficiently requires an understanding of the chemical thermodynamics of gas hydrate formation. In order to forecast the temperature of gas hydrate formation, the current investigation compares the effectiveness of three different types of machine learning algorithms: Support Vector Regression (SVR), Artificial Neural Networks (ANNs), and Decision Tree Regression (DT). The research was conducted using Python 3.11.3 as the programming framework, which made use of its extensive ecosystem of open-source tools, including scikit-learn (version 1.2.2) and Keras with TensorFlow. With ANNs, there was no activation function in the output layer and the hyperbolic tangent function was used as the activation function in a hidden layer. The Radial Basis Function (rbf) was used as the Kernel function for Support Vector Regression (SVR). A maximum tree depth of 15 was imposed on the Decision Tree (DT) regression. Throughout the whole dataset, evaluation measures such as Root Mean Square Error (RMSE) and coefficient of determination (R2) were calculated. The findings showed that the R2/RMSE values for SVR, ANNs, and DT regression were, respectively, (0.9999, 0.0631), (0.9986, 0.5011), and (0.9278, 3.5606). In conclusion, the models' output was rated as follows in descending order: Support vector regression (SVR) is a subset of decision tree regression (DT) and artificial neural networks (ANNs). Following that, a Web User Interface (WUI) was created using the Decision Tree paradigm, which proved to be the most efficient. In theoretical terms, this work opens the door to further developments in gas engineering. The prediction capability of the models could potentially further improved by adding more experimental data to the dataset used for training.